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Genetic Screens

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...

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Related Experiment Video

Updated: Mar 29, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

iRDA: a new filter towards predictive, stable, and enriched candidate genes.

Hung-Ming Lai¹, Andreas A Albrecht², Kathleen K Steinhöfel³

¹Algorithms and Bioinformatics Research Group, Department of Informatics, King's College London, Strand, London, WC2R 2LS, UK. hung-ming.lai@kcl.ac.uk.

|December 10, 2015

Summary

iRDA is a new filter for discovering gene expression markers. It identifies predictive, stable, and enriched genes for disease diagnosis, outperforming existing methods in cancer and disease studies.

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Last Updated: Mar 29, 2026

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput screening (HTS) enables gene expression profiling for identifying prognostic gene signatures and biological markers.
Existing feature selection methods and information theoretic filters aim to capture gene relevance and correlations for microarray classification.

Purpose of the Study:

To introduce and formulate iRDA, a novel multivariate filter for discovering high-throughput screening (HTS) gene-expression candidate genes.
To incorporate feature relevance, redundancy, and interdependence for higher-order gene interactions.

Main Methods:

iRDA employs a four-step framework utilizing approximate Markov blankets and information theory.
Heuristic search strategies (forward, backward, insertion) are used to identify gene interactions.
The method considers feature relevance, redundancy, and interdependence in feature-pair contexts.

Main Results:

iRDA demonstrated superior classification performance compared to three popular information theoretic filters across seven cancer and four other disease datasets.
Stability measures indicated iRDA as the most robust filter with minimal variance.
Gene Set Enrichment Analysis (GSEA) revealed statistically enriched gene sets for iRDA on five of six benchmark datasets.

Conclusions:

iRDA is a promising filter for identifying predictive, stable, and enriched gene-expression candidate genes.
The filter's performance in classification, stability, and enrichment analysis supports its utility in disease diagnosis and biomarker discovery.